Radiologists' perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study.

In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research focuses on the technical features of AI rather than its real-world clinical implementation. To evaluate the implementation process of an AI-based computer-aided detection system (AI-CAD) for prostate MRI readings, we interviewed German radiologists in a pre-post design. We embedded our findings in the Model of Workflow Integration and the Technology Acceptance Model to analyze workflow effects, facilitators, and barriers. The most prominent barriers were: (i) a time delay in the work process, (ii) additional work steps to be taken, and (iii) an unstable performance of the AI-CAD. Most frequently named facilitators were (i) good self-organization, and (ii) good usability of the software. Our results underline the importance of a holistic approach to AI implementation considering the sociotechnical work system and provide valuable insights into key factors of the successful adoption of AI technologies in work systems.

Prospective effects of an artificial intelligence-based computer-aided detection system for prostate imaging on routine workflow and radiologists' outcomes.

OBJECTIVES: Artificial intelligence (AI) is expected to alleviate the negative consequences of rising case numbers for radiologists. Currently, systematic evaluations of the impact of AI solutions in real-world radiological practice are missing. Our study addresses this gap by investigating the impact of the clinical implementation of an AI-based computer-aided detection system (CAD) for prostate MRI reading on clinicians' workflow, workflow throughput times, workload, and stress. MATERIALS AND METHODS: CAD was newly implemented into radiology workflow and accompanied by a prospective pre-post study design. We assessed prostate MRI case readings using standardized work observations and questionnaires. The observation period was three months each in a single department. Workflow throughput times, PI-RADS score, CAD usage and radiologists' self-reported workload and stress were recorded. Linear mixed models were employed for effect identification. RESULTS: In data analyses, 91 observed case readings (pre: 50, post: 41) were included. Variation of routine workflow was observed following CAD implementation. A non-significant increase in overall workflow throughput time was associated with CAD implementation (mean 16.99 ± 6.21 vs 18.77 ± 9.69 min, p = .51), along with an increase in diagnostic reading time for high suspicion cases (mean 15.73 ± 4.99 vs 23.07 ± 8.75 min, p = .02). Changes in radiologists' self-reported workload or stress were not found. CONCLUSION: Implementation of an AI-based detection aid was associated with lower standardization and no effects over time on radiologists' workload or stress. Expectations of AI decreasing the workload of radiologists were not confirmed by our real-world study. PRE-REGISTRATION: German register for clinical trials https://drks.de/; DRKS00027391.

Job demands at the patient's bedside and their effects on stress and satisfaction of nurses.

AIM: The study aimed to assess job demands at the patient's bedside and to evaluate the contribution of this central workplace to the stress and satisfaction of nurses. DESIGN: In this cross-sectional survey study, a questionnaire was compiled and all registered nurses from intensive, general and intermediate care wards at a large German hospital were invited to participate. METHODS: The questionnaire used a list of care activities to assess nurses' workload at the patient's bed. The German Copenhagen Psychosocial Questionnaire and an adapted version of the German Perceived Stress Scale were used to measure nurses' stress and burn-out, and single items to assess health status, organisational commitment, job satisfaction, and satisfaction with the quality of care. The questionnaire was returned by 389 nurses. RESULTS: Expected correlations of workload at the patient's bed with stress, burn-out and satisfaction of the nurses were shown. A moderating effect of organisational commitment was non-existent but was shown for the self-assessed health on the correlation between workload and satisfaction with the quality of care. Organisational commitment correlated negatively with stress and burn-out and positively with satisfaction. The study provides evidence that rates of burn-out and stress do not differ based on the work area of nurses. Because job demands at the patient's bed correlated with all outcomes, measures to improve this specific workspace are sensible.

Integration of Artificial Intelligence Into Sociotechnical Work Systems-Effects of Artificial Intelligence Solutions in Medical Imaging on Clinical Efficiency: Protocol for a Systematic Literature Review.

BACKGROUND: When introducing artificial intelligence (AI) into clinical care, one of the main objectives is to improve workflow efficiency because AI-based solutions are expected to take over or support routine tasks. OBJECTIVE: This study sought to synthesize the current knowledge base on how the use of AI technologies for medical imaging affects efficiency and what facilitators or barriers moderating the impact of AI implementation have been reported. METHODS: In this systematic literature review, comprehensive literature searches will be performed in relevant electronic databases, including PubMed/MEDLINE, Embase, PsycINFO, Web of Science, IEEE Xplore, and CENTRAL. Studies in English and German published from 2000 onwards will be included. The following inclusion criteria will be applied: empirical studies targeting the workflow integration or adoption of AI-based software in medical imaging used for diagnostic purposes in a health care setting. The efficiency outcomes of interest include workflow adaptation, time to complete tasks, and workload. Two reviewers will independently screen all retrieved records, full-text articles, and extract data. The study's methodological quality will be appraised using suitable tools. The findings will be described qualitatively, and a meta-analysis will be performed, if possible. Furthermore, a narrative synthesis approach that focuses on work system factors affecting the integration of AI technologies reported in eligible studies will be adopted. RESULTS: This review is anticipated to begin in September 2022 and will be completed in April 2023. CONCLUSIONS: This systematic review and synthesis aims to summarize the existing knowledge on efficiency improvements in medical imaging through the integration of AI into clinical workflows. Moreover, it will extract the facilitators and barriers of the AI implementation process in clinical care settings. Therefore, our findings have implications for future clinical implementation processes of AI-based solutions, with a particular focus on diagnostic procedures. This review is additionally expected to identify research gaps regarding the focus on seamless workflow integration of novel technologies in clinical settings. TRIAL REGISTRATION: PROSPERO CRD42022303439; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=303439. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40485.